def slpFuncY(mslpMean, mslpStd, mslppmm, datefhour, dateArr, lats, lons, date,
ind):
subsetPerc = np.ones_like(mslpMean)
totalPerc = np.ones_like(mslpMean)
ssaAnom = np.ones_like(mslpMean)
saAnom = np.ones_like(mslpMean)
if datetime.now().month >= 3 and datetime.now().month <= 5:
mArr, sArr = mc.mcliLoad(var='mslp', ind=ind, notDJF='MAM')
elif datetime.now().month >= 6 and datetime.now().month <= 8:
mArr, sArr = mc.mcliLoad(var='mslp', ind=ind, notDJF='JJA')
else:
mArr, sArr = mc.mcliLoad(var='mslp', ind=ind)
for i in range(0, len(mslpMean)):
subsetPerc[i], totalPerc[i], ssaAnom[i] = mc.subsetMCli(
mslpMean[i], mslpStd[i], mArr[:, i], sArr[:, i])
a = netCDFSSA(ssaAnom[datefhour % 24 == 0],
(date.toordinal() + date.hour / 24.))
a.detNewApp()
print 'completed SSA'
print 'starting slp plots'
if datetime.now().month >= 3 and datetime.now().month <= 5:
[
pt.slpplotMaker(date, mslpMean[i], mslpStd[i], datefhour[i],
dateArr[i], ssaAnom[i], subsetPerc[i],
totalPerc[i], mslppmm[i], lats, lons)
for i in range(0, len(mslpMean))
]
elif datetime.now().month >= 6 and datetime.now().month <= 8:
[
pt.slpplotMaker(date, mslpMean[i], mslpStd[i], datefhour[i],
dateArr[i], ssaAnom[i], subsetPerc[i],
totalPerc[i], mslppmm[i], lats, lons)
for i in range(0, len(mslpMean))
]
else:
[
pt.slpplotMakerY(date, mslpMean[i], mslpStd[i], datefhour[i],
dateArr[i], mslppmm[i], lats, lons)
for i in range(0, len(mslpMean))
]
gc.collect()
def slpFuncHist(mslpMean, mslpStd, mslppmm, datefhour, dateArr,
lats, lons, date, ind):
subsetPerc = np.ones_like(mslpMean)
totalPerc = np.ones_like(mslpMean)
ssaAnom = np.ones_like(mslpMean)
mArr,sArr = mc.mcliLoad(var='mslp', ind=ind)
for i in range(0, len(mslpMean)):
subsetPerc[i], totalPerc[i], ssaAnom[i] = mc.subsetMCli(mslpMean[i], mslpStd[i], mArr[:,i], sArr[:, i])
print 'starting slp plots'
[slpplotMaker(date, mslpMean[i], mslpStd[i], datefhour[i],
dateArr[i], ssaAnom[i], subsetPerc[i], totalPerc[i], mslppmm[i],
lats, lons) for i in range(0, len(mslpMean))]
gc.collect()
def slpFunc(mslpMean, mslpStd, mslppmm, datefhour, dateArr, lats, lons, date,
ind):
subsetPerc = np.ones_like(mslpMean)
totalPerc = np.ones_like(mslpMean)
ssaAnom = np.ones_like(mslpMean)
saAnom = np.ones_like(mslpMean)
if datetime.now().month >= 3 or datetime.now().month <= 5:
mArr, sArr = mc.mcliLoad(var='mslp', ind=ind, notDJF='MAM')
mArr, sArr = mc.mcliLoad(var='mslp', ind=ind)
for i in range(0, len(mslpMean)):
subsetPerc[i], totalPerc[i], ssaAnom[i], saAnom[i] = mc.subsetMCli(
mslpMean[i], mslpStd[i], mArr[:, i], sArr[:, i])
print 'starting slp plots'
[
pt.slpplotMaker(date, mslpMean[i], mslpStd[i], datefhour[i],
dateArr[i], ssaAnom[i], subsetPerc[i], totalPerc[i],
mslppmm[i], lats, lons)
for i in range(0, len(dateArr))
]
gc.collect()
datafunc(ssaAnom)
def hgtFunc(hgtMean, hgtStd, hgtpmm, datefhour, dateArr, lats, lons, date,
ind):
subsetPerc = np.ones_like(hgtMean)
totalPerc = np.ones_like(hgtMean)
ssaAnom = np.ones_like(hgtMean)
saAnom = np.ones_like(hgtMean)
mArr, sArr = mc.mcliLoad(var='500hgt', ind=ind)
for i in range(0, len(hgtMean)):
subsetPerc[i], totalPerc[i], ssaAnom[i], saAnom[i] = mc.subsetMCli(
hgtMean[i], hgtStd[i], mArr[:, i], sArr[:, i])
print 'starting hgt plots'
[
pt.hgtplotMaker(date, hgtMean[i], hgtStd[i], datefhour[i], dateArr[i],
ssaAnom[i], subsetPerc[i], totalPerc[i], hgtpmm[i],
lats, lons) for i in range(0, len(hgtMean))
]
gc.collect()
def tmpFunc(tmpMean, tmpStd, tmppmm, datefhour, dateArr, lats, lons, date,
ind):
subsetPerc = np.ones_like(tmpMean)
totalPerc = np.ones_like(tmpMean)
ssaAnom = np.ones_like(tmpMean)
saAnom = np.ones_like(tmpMean)
mArr, sArr = mc.mcliLoad(var='850tmp', ind=ind)
for i in range(0, len(tmpMean)):
subsetPerc[i], totalPerc[i], ssaAnom[i], saAnom[i] = mc.subsetMCli(
tmpMean[i], tmpStd[i], mArr[:, i], sArr[:, i])
print 'starting tmp plots'
[
pt.tmpplotMaker(date, tmpMean[i], tmpStd[i], datefhour[i], dateArr[i],
ssaAnom[i], subsetPerc[i], totalPerc[i], tmppmm[i],
lats, lons) for i in range(0, len(tmpMean))
]
gc.collect()